Hybrid descriptors–conjoint indices: a case study on imidazole-thiourea containing glutaminyl cyclase inhibitors for design of novel anti-Alzheimer’s candidates

References

• A. Alzheimer, R.A. Stelzmann, H.N. Schnitzlein, and F.R. Murtagh, An English translation of Alzheimer’s 1907 paper, “Uber eine eigenartige Erkankung der Hirnrinde”, Clin. Anat. 8 (1995), pp. 429–431. doi:10.1002/ca.980080612.
   PubMedGoogle Scholar
• M.A. DeTure and D.W. Dickson, The neuropathological diagnosis of Alzheimer’s disease, Mol. Neurodegener. 14 (2019), pp. 1–18. doi:10.1186/s13024-019-0333-5.
   PubMed Web of Science ®Google Scholar
• M.S. Parihar and T. Hemnani, Alzheimer’s disease pathogenesis and therapeutic interventions, J. Clin. Neurosci. 11 (2004), pp. 456–467. doi:10.1016/j.jocn.2003.12.007.
   PubMed Web of Science ®Google Scholar
• S. Chakrabarti, V.K. Khemka, A. Banerjee, G. Chatterjee, A. Ganguly, and A. Biswas, Metabolic risk factors of sporadic Alzheimer’s disease: Implications in the pathology, pathogenesis and treatment, Aging Dis. 6 (2015), pp. 282–299. doi:10.14336/AD.2014.002.
   PubMed Web of Science ®Google Scholar
• K. Lao, N. Ji, X. Zhang, W. Qiao, Z. Tang, and X. Gou, Drug development for Alzheimer’s disease: Review, J. Drug Target 27 (2019), pp. 164–173. doi:10.1080/1061186X.2018.1474361.
   PubMed Web of Science ®Google Scholar
• Alzheimer’s Association, Alzheimer’s disease facts and figures, Alzheimer’s Dement. 17 (2021), pp. 327–406.
   PubMed Web of Science ®Google Scholar
• Z. Breijyeh and R. Karaman, Comprehensive review on Alzheimer’s disease: Causes and treatment, Molecules 25 (2020), pp. 5789. doi:10.3390/molecules25245789.
   PubMed Web of Science ®Google Scholar
• K.G. Yiannopoulou and S.G. Papageorgiou, Current and future treatments in Alzheimer disease: An update, J. Cent. Nerv. Syst. Dis. 12 (2020), pp. 117957352090739. doi:10.1177/1179573520907397.
   Web of Science ®Google Scholar
• H. Hillen, The beta amyloid dysfunction (bad) hypothesis for Alzheimer’s disease, Front. Neurosci. 13 (2019), pp. 1154. doi:10.3389/fnins.2019.01154.
   PubMed Web of Science ®Google Scholar
• Y. Harigaya, T.C. Saido, C.B. Eckman, C.M. Prada, M. Shoji, and S.G. Younkin, Amyloid beta protein starting pyroglutamate at position 3 is a major component of the amyloid deposits in the Alzheimer’s disease brain, Biochem. Biophys. Res. Commun. 24 (2000), pp. 422–427. doi:10.1006/bbrc.2000.3490.
   Google Scholar
• C. Russo, T.C. Saido, L.M. DeBusk, M. Tabaton, P. Gambetti, and J.K. Teller, Heterogeneity of water-soluble amyloid beta-peptide in Alzheimer’s disease and Down’s syndrome brains, FEBS Lett. 409 (1997), pp. 411–416. doi:10.1016/S0014-5793(97)00564-4.
   PubMed Web of Science ®Google Scholar
• R. Perez-Garmendia and G. Gevorkian, Pyroglutamate-modified amyloid beta peptides: Emerging targets for Alzheimer´s disease Immunotherapy, Curr. Neuropharmacol. 11 (2013), pp. 491–498. doi:10.2174/1570159X11311050004.
   PubMed Web of Science ®Google Scholar
• M. Morawski, S. Schilling, M. Kreuzberger, A. Waniek, C. Jäger, B. Koch, H. Cynis, A. Kehlen, T. Arendt, M. Hartlage-Rübsamen, H.U. Demuth, and S. Roßner, Glutaminyl cyclase in human cortex: Correlation with (pGlu)-amyloid-β load and cognitive decline in Alzheimer’s disease, J. Alzheimers Dis. 39 (2014), pp. 385–400. doi:10.3233/JAD-131535.
   PubMed Web of Science ®Google Scholar
• Y.M. Kuo, M.R. Emmerling, A.S. Woods, R.J. Cotter, and A.E. Roher, Isolation, chemical characterization, and quantitation of a beta 3-pyroglutamyl peptide from neuritic plaques and vascular amyloid deposits, Biochem. Biophys. Res. Commun. 237 (1997), pp. 188–191. doi:10.1006/bbrc.1997.7083.
   PubMed Web of Science ®Google Scholar
• A. Becker, S. Kohlmann, A. Alexandru, W. Jagla, F. Canneva, C. Bäuscher, H. Cynis, R. Sedlmeier, S. Graubner, S. Schilling, H.U. Demuth, and S.V. Hörsten, Glutaminyl cyclase-mediated toxicity of pyroglutamate-beta amyloid induces striatal neurodegeneration, BMC Neurosci. 14 (2013), pp. 1–18. doi:10.1186/1471-2202-14-108.
   PubMed Web of Science ®Google Scholar
• C.H. Hennekens, B.A. Bensadon, R. Zivin, and J.M. Gaziano, Hypothesis: Glutaminyl Cyclase inhibitors decrease risks of Alzheimer’s disease and related dementias, Expert Rev. Neurother. 15 (2015), pp. 1245–1248. doi:10.1586/14737175.2015.1088784.
   PubMed Web of Science ®Google Scholar
• P. Scheltens, M. Hallikainen, T. Grimmer, T. Duning, A.A. Gouw, C.E. Teunissen, A.M. Wink, P. Maruff, J. Harrison, C.M. van Baal, S. Bruins, I. Lues, and N.D. Prins, Safety, tolerability and efficacy of the Glutaminyl cyclase inhibitor PQ912 in Alzheimer’s disease: Results of a randomized, double-blind, placebo-controlled phase 2a study, Alzheimers Res. Ther. 10 (2018), pp. 1–14. doi:10.1186/s13195-018-0431-6.
   PubMed Web of Science ®Google Scholar
• T. Hoffmann, A. Meyer, U. Heiser, S. Kurat, L. Böhme, M. Kleinschmidt, K.U. Bühring, B. Hutter-Paier, M. Farcher, H.U. Demuth, I. Lues, and S. Schilling, Glutaminyl cyclase inhibitor PQ912 improves cognition in mouse models of Alzheimer’s disease-studies on relation to effective target occupancy, J. Pharmacol. Exp. Ther. 362 (2017), pp. 119–130. doi:10.1124/jpet.117.240614.
   PubMed Web of Science ®Google Scholar
• S. Schilling, U. Zeitschel, T. Hoffmann, U. Heiser, M. Francke, A. Kehlen, M. Holzer, B. Hutter-Paier, M. Prokesch, M. Windisch, W. Jagla, D. Schlenzig, C. Lindner, T. Rudolph, G. Reuter, H. Cynis, D. Montag, H.U. Demuth, and S. Rossner, Glutaminyl cyclase inhibition attenuates pyroglutamate Aβ and Alzheimer’s disease-like pathology, Nat. Med. 14 (2008), pp. 1106–1111. doi:10.1038/nm.1872.
   PubMed Web of Science ®Google Scholar
• H. Song, Y.J. Chang, M. Moon, S.K. Park, P.T. Tran, V.H. Hoang, J. Lee, and I. Mook-Jung, Inhibition of Glutaminyl cyclase ameliorates amyloid pathology in an animal model of Alzheimer’s disease via the modulation of γ-secretase activity, J. Alzheimers Dis. 43 (2015), pp. 797–807. doi:10.3233/JAD-141356.
   PubMed Web of Science ®Google Scholar
• H. Cynis, T. Hoffmann, D. Friedrich, A. Kehlen, K. Gans, M. Kleinschmidt, J.U. Rahfeld, R. Wolf, M. Wermann, A. Stephan, M. Haegele, R. Sedlmeier, S. Graubner, W. Jagla, A. Müller, R. Eichentopf, U. Heiser, F. Seifert, P.H. Quax, M.R. de Vries, I. Hesse, D. Trautwein, U. Wollert, S. Berg, E.J. Freyse, S. Schilling, and H.U. Demuth, The isoenzyme of glutaminyl cyclase is an important regulator of monocyte infiltration under inflammatory conditions, EMBO Mol. Med. 3 (2011), pp. 545–558. doi:10.1002/emmm.201100158.
   PubMed Web of Science ®Google Scholar
• A. Talevi, Computer-aided drug design: An overview, in Computational Drug Discovery and Design. Methods in Molecular Biology, M. Gore and U. Jagtap, eds., Humana Press, New York, 2018, pp. 1–19.
   Google Scholar
• M.H. Baig, K. Ahmad, S. Roy, J.M. Ashraf, M. Adil, M.H. Siddiqui, S. Khan, M.A. Kamal, I. Provazník, and I. Choi, Computer aided drug design: Success and limitations, Curr. Pharma. Des. 22 (2016), pp. 572–581. doi:10.2174/1381612822666151125000550.
   PubMed Web of Science ®Google Scholar
• B.J. Neves, R.C. Braga, C.C. Melo-Filho, J.T. Moreira-Filho, E.N. Muratov, and C.H. Andrade, QSAR-Based virtual Screening: Advances and applications in drug discovery, Front. Pharmacol. 9 (2018), pp. 1275. doi:10.3389/fphar.2018.01275.
   PubMed Web of Science ®Google Scholar
• D.A. Winkler, The role of quantitative structure–activity relationships (QSAR) in biomolecular discovery, Brief. Bioinform. 3 (2002), pp. 73–86. doi:10.1093/bib/3.1.73.
   PubMedGoogle Scholar
• R. Munuganti, N. Yadavalli, S. Nunna, and S. Mahmood, 3D-QSAR CoMFA study on human glutaminyl cyclase inhibitors, IEJMD 6 (2007), pp. 320–330.
   Google Scholar
• V. Kumar, M.K. Gupta, G. Singh, and Y.S. Prabhakar, CP-MLR/PLS directed QSAR study on the glutaminyl cyclase inhibitory activity of imidazoles: Rationales to advance the understanding of activity profile, J. Enz. Inhib. Med. Chem. 28 (2013), pp. 515–522. doi:10.3109/14756366.2011.654111.
   PubMed Web of Science ®Google Scholar
• O.H.A. Al-Attraqchi and K.N. Venugopala, 2D- and 3D-QSAR modeling of imidazole-based glutaminyl cyclase inhibitors, Curr. Comput. Aided Drug Des. 16 (2020), pp. 682–697. doi:10.2174/1573409915666190918150136.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, I. Raška, E. Benfenati, and G.C. Gini, Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method, Cent. Eur. J. Chem. 11 (2013), pp. 371–380.
   Google Scholar
• A.A. Toropov, A.P. Toropova, I. Raska, D. Leszczynska, and J. Leszczynski, Comprehension of drug toxicity: Software and databases, Comput. Biol. Med. 45 (2014), pp. 20–25. doi:10.1016/j.compbiomed.2013.11.013.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, F. Como, and E. Benfenati, Quantitative structure–activity relationship models for bee toxicity, Toxicol. Environ. Chem. 99 (2016), pp. 1117–1128.
   Web of Science ®Google Scholar
• A. Kumar and S. Chauhan, Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors, SAR QSAR Environ. Res. 28 (2017), pp. 179–197. doi:10.1080/1062936X.2017.1293729.
   PubMed Web of Science ®Google Scholar
• P. Kumar, A. Kumar, J. Sindhu, and S. Lal, QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method, Drug Res. 69 (2019), pp. 159–167. doi:10.1055/a-0652-5290.
   PubMed Web of Science ®Google Scholar
• M.S. Chauhan, A. Kumar, and A. Kumar, Development of prediction model for fructose-1,6- bisphosphatase inhibitors using the Monte Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 145–159. doi:10.1080/1062936X.2019.1568299.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, H. Ghanbari, S. Lotfi, and N. Azimi, Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method, Mol. Divers. 25 (2021), pp. 87–97. doi:10.1007/s11030-019-10026-9.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, Monte Carlo method based QSAR studies of Mer Kinase Inhibitors in compliance with OECD Principles, Drug Res. 68 (2018), pp. 189–195. doi:10.1055/s-0043-119288.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov and A.P. Toropova, The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? Mutat. Res. Genet. Toxicol. Environ. Mutagen. 819 (2017), pp. 31–37. doi:10.1016/j.mrgentox.2017.05.008.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov and A.P. Toropova, Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation, Anticancer Res. 38 (2018), pp. 6189–6194. doi:10.21873/anticanres.12972.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, I. Raška, A.P. Toropova, M. Raškova, A.M. Veselinović, and J.B. Veselinović, The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models, Sci. Total Environ. 659 (2019), pp. 1387–1394. doi:10.1016/j.scitotenv.2018.12.439.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, Nucleobase sequence-based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method, J. Biomol. Struct. Dyn. 38 (2020), pp. 3296–3306. doi:10.1080/07391102.2019.1656109.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov and A.P. Toropova, Correlation intensity index: Building up models for mutagenicity of silver nanoparticles, Sci. Total Environ. 737 (2020), pp. 139720. doi:10.1016/j.scitotenv.2020.139720.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, Correlation intensity index (CII) as a benchmark of predictive potential: Construction of quantitative structure activity relationship models for anti-influenza single-stranded DNA aptamers using Monte Carlo optimization, J. Mol. Struct. 1246 (2021), pp. 131205. doi:10.1016/j.molstruc.2021.131205.
   Web of Science ®Google Scholar
• V.H. Hoang, P.T. Tran, M. Cui, V.T. Ngo, K. Choi, K. Choi, K. Choi, H. Kim, H. Kim, H.J. Ha, H.S. Hong, Y.H. Kim, Y.H. Kim, and J. Lee, Discovery of potent human glutaminyl cyclase inhibitors as anti-Alzheimer’s agents based on rational design, J. Med. Chem. 60 (2017), pp. 2573–2590. doi:10.1021/acs.jmedchem.7b00098.
   PubMedGoogle Scholar
• V.H. Hoang, V.T.H. Ngo, M. Cui, N.V. Manh, P.T. Tran, H.J. Ha, Y.H. Chang, Y.H. Chang, Y.H. Chang, Y.H. Chang, S.J.Y. Macalino, J. Lee, J. Lee, and J. Lee, Discovery of conformationally restricted human glutaminyl cyclase inhibitors as potent anti-Alzheimer’s agents by structure-based design, J. Med. Chem. 62 (2019), pp. 8011–8027. doi:10.1021/acs.jmedchem.9b00751.
   PubMed Web of Science ®Google Scholar
• V.T.H. Ngo, V.H. Hoang, P.T. Tran, J. Ann, M. Cui, G. Park, S. Choi, J. Lee, H. Kim, H.J. Ha, K. Choi, Y.H. Kim, and J. Lee, Potent human glutaminyl cyclase inhibitors as potential anti-Alzheimer’s agents: Structure-activity relationship study of Arg-mimetic region, Bioorg. Med. Chem. 26 (2018), pp. 1035–1049. doi:10.1016/j.bmc.2018.01.015.
   PubMed Web of Science ®Google Scholar
• V.T.H. Ngo, V.H. Hoang, P.T. Tran, N.V. Manh, J. Ann, E. Kim, M. Cui, S. Choi, J. Lee, H. Kim, H.J. Ha, K. Choi, Y.H. Kim, and J. Lee, Structure-activity relationship investigation of Phe-Arg mimetic region of human glutaminyl cyclase inhibitors, Bioorg. Med. Chem. 26 (2018), pp. 3133–3144. doi:10.1016/j.bmc.2018.04.040.
   PubMed Web of Science ®Google Scholar
• A.M. Veselinović, A.A. Toropov, A.P. Toropova, D. Stanković-Đorđević, and J.B. Veselinovic, Design and development of novel antibiotics based on FtsZ inhibition – In silico studies, New J. Chem. 42 (2018), pp. 10976–10982. doi:10.1039/C8NJ01034J.
   Web of Science ®Google Scholar
• Marvin-Sketch-v.14.11.17.0, ChemAxon, XhemAxon KFT, Budapest, Hungary, 2014.
   Google Scholar
• N. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, and G.R. Hutchison, Open babel: An open chemical toolbox, J. Cheminform. 3 (2011), pp. 33. doi:10.1186/1758-2946-3-33.
   PubMed Web of Science ®Google Scholar
• A. Kumar and S. Chauhan, QSAR differential model for prediction of SIRT1 modulation using Monte Carlo method, Drug Res. 67 (2017), pp. 156–162.
   Web of Science ®Google Scholar
• A. Kumar and S. Chauhan, Use of simplified molecular input line entry system and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors, Fut. Med. Chem. 10 (2018), pp. 1603–1622. doi:10.4155/fmc-2018-0024.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, S.E. Martyanov, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines, Chemom. Intell. Lab. Syst. 109 (2011), pp. 94–100. doi:10.1016/j.chemolab.2011.07.008.
   Web of Science ®Google Scholar
• S. Jain, B. Bhardwaj, S.K.A. Amin, N. Adhikari, T. Jha, and S. Gayen, Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modelling, J. Biomol. Struct. Dyn. 38 (2020), pp. 1683–1696. doi:10.1080/07391102.2019.1615000.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati, Prediction of biochemical endpoints by the CORAL software: Prejudices, paradoxes, and results, in Computational Toxicology. Methods in Molecular Biology, O. Nicolotti, ed., Springer Nature, New York, 2018, pp. 573–583.
   Google Scholar
• S. Jain, S.A. Amin, N. Adhikari, T. Jha, and S. Gayen, Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: Identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study, J. Biomol. Struct. Dyn. 38 (2020), pp. 66–77. doi:10.1080/07391102.2019.1566093.
   PubMed Web of Science ®Google Scholar
• A.P. Toropova, A.A. Toropov, J. Leszczynski, and N. Sizochenko, Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions, Environ. Toxicol. Pharmacol. 86 (2021), pp. 103665. doi:10.1016/j.etap.2021.103665.
   PubMed Web of Science ®Google Scholar
• A. Kumar and P. Kumar, Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking, Struct. Chem. 32 (2021), pp. 149–165. doi:10.1007/s11224-020-01629-2.
   Web of Science ®Google Scholar
• K. Bagri, A. Kumar, M. Nimbhal, and P. Kumar, Index of ideality of correlation and correlation contradiction index: A confluent perusal on acetylcholinesterase inhibitors, Mol. Simul. 46 (2020), pp. 777–786. doi:10.1080/08927022.2020.1770753.
   Web of Science ®Google Scholar
• A.P. Toropova and A. Toropov, QSPR and nano-QSPR: What is the difference? J. Mol. Struct. 1182 (2019), pp. 141–149. doi:10.1016/j.molstruc.2019.01.040.
   Web of Science ®Google Scholar
• A. Kumar, J. Sindhu, and P. Kumar, In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N -myristoyltransferase using Monte Carlo method with index of ideality of correlation, J. Biomol. Struct. Dyn. 39 (2021), pp. 5014–5025. doi:10.1080/07391102.2020.1784286.
   PubMed Web of Science ®Google Scholar
• M. Nimbhal, K. Bagri, P. Kumar, and A. Kumar, The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators, Struct. Chem. 31 (2020), pp. 831–839. doi:10.1007/s11224-019-01468-w.
   Web of Science ®Google Scholar
• A. Kumar and P. Kumar, Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in Monte Carlo optimization, Arch. Toxicol. 94 (2020), pp. 3069–3086. doi:10.1007/s00204-020-02828-w.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, A.P. Toropova, and A.A. Toropov, Correlation intensity index: Mathematical modeling of cytotoxicity of metal oxide nanoparticles, Nanotoxicology 14 (2020), pp. 1118–1126. doi:10.1080/17435390.2020.1808252.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, N. Sizochenko, A.P. Toropova, D. Leszczynska, and J. Leszczynski, Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII), J. Mol. Liq. 317 (2020), pp. 113929. doi:10.1016/j.molliq.2020.113929.
   Web of Science ®Google Scholar
• A.P. Toropova and A.A. Toropov, Fullerenes C 60 and C 70: A model for solubility by applying the correlation intensity index, Fuller. Nanotub. Carbon Nanostruct. 28 (2020), pp. 900–906. doi:10.1080/1536383X.2020.1779705.
   Web of Science ®Google Scholar
• K. Roy, On some aspects of validation of predictive quantitative structure-activity relationship models, Expert Opin. Drug Discov. 2 (2007), pp. 1567–1577. doi:10.1517/17460441.2.12.1567.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, On the development and validation of QSAR models, in Computational Toxicology, Methods in Molecular Biology, B. Reisfeld and A. Mayeno, eds., Vol. 930, Humana Press, Totowa, New Jersey, 2013, pp. 499–526.
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2! J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:10.1016/S1093-3263(01)00123-1.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701. doi:10.1002/qsar.200610151.
   Google Scholar
• V. Consonni, D. Ballabio, and R. Todeschini, Evaluation of model predictive ability by external validation techniques, J. Chemom. 24 (2010), pp. 194–201. doi:10.1002/cem.1290.
   Web of Science ®Google Scholar
• K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. Syst. 152 (2016), pp. 18–33. doi:10.1016/j.chemolab.2016.01.008.
   Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemom. Intell. Lab. Syst. 145 (2015), pp. 22–29. doi:10.1016/j.chemolab.2015.04.013.
   Web of Science ®Google Scholar
• K. Roy and S. Kar, Importance of applicability domain of QSAR models, in Quantitative structure-activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, P.A. Hershey, ed., IGI Global, USA, 2015, pp. 180–211.
   Google Scholar
• O. Trott and A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem. 31 (2010), pp. 455–461. doi:10.1002/jcc.21334.
   PubMed Web of Science ®Google Scholar
• Discovery studio visulizer. Dassault-Systèmes-BIOVIA, Dassault Systèmes, San Diego, 2019.
   Google Scholar